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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →An agentic fraud investigation system can use TigerGraph to find connected evidence, LangGraph to manage investigation steps, and an LLM to interpret the evidence—while deterministic policy code controls which actions are allowed. Kishan Vyas’s Hacker House Goa project describes that architecture as a way to turn a transaction alert into a reasoned case file, gather more context when evidence is incomplete, and record how recommendations change. Its reported results are project claims, not an independent audit.
What the project set out to build
In his DEV Community account, Kishan Vyas describes a TigerGraph-first fraud investigation platform developed for the TigerGraph × Hacker House Goa hackathon. The goal was to investigate transaction alerts and produce case files, SAR reports when the project’s criteria were met, and auditable recommendations before and after additional evidence was collected. The benchmark used six months of card transactions from the IEEE-CIS Vesta dataset alongside historical closed investigations.
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The investigation is organized around four questions: what kind of fraud might be occurring, how far the connected network extends, what the financial exposure is, and what action bank policy permits. Examples in the project include blocking an account, requesting step-up authentication, seeking managerial approval, or filing a suspicious activity report (SAR).
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How the components divide the work
The architecture assigns distinct responsibilities rather than asking a language model to do everything. TigerGraph and custom GSQL queries handle graph relationships and calculated network signals. LangGraph coordinates workflow state, evidence-gathering loops, sufficiency checks, and human-approval interrupts. An LLM synthesizes gathered evidence, while code-level policy rules authorize or block proposed actions.
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- Graph layer: answers structured questions about connections, paths, and money flows.
- Workflow layer: tracks what is known, requests additional context, and manages approval steps.
- Language-model layer: interprets the collected evidence and helps explain a case.
- Policy layer: applies explicit rules before a consequential recommendation or simulated action proceeds.
The article also describes a TigerGraph MCP adapter for tool access, GraphRAG-based precedent memory, and an analyst dashboard built with Next.js, Cytoscape.js, and server-sent events.
What graph queries contribute to an investigation
A transaction alert is a starting point, not a complete explanation. The project’s graph queries look for accounts that share devices or IP-related attributes, calculate paths between alerts and confirmed-fraud vertices, inspect money-flow patterns and cycles, and return bounded neighborhoods for visualization. Those operations can surface links that are hard to assess one transaction at a time.
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Keeping counts, pathfinding, and relationship calculations in graph queries also makes their outputs more deterministic than asking an LLM to infer them from a long text prompt. The model can then interpret those results rather than inventing connections or doing arithmetic from prose. That division does not, by itself, establish that every output is correct: the evidence still needs to be complete, relevant, and accurately represented.
How the workflow handles missing evidence and uncertainty
The described system tracks risk level, confidence, and evidence completeness. When risk is high but evidence completeness is low, the workflow may seek targeted context through structured tools rather than treating the first alert as sufficient. The article says completed case states are written back as memory so later investigations can retrieve similar cases.
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It also describes storing the recommended next-best action before and after new evidence is gathered. That record can make a recommendation change visible to an analyst: what the system proposed initially, what additional information arrived, and how that information affected the next step. In this design, the workflow’s value is not only a final label; it is the trail connecting evidence to a changing recommendation.
Why authorization should remain separate from LLM synthesis
An LLM can summarize evidence and propose a next step, but a proposal is not authorization. In Vyas’s account, deterministic bank-policy checks stand between a recommendation and an action or simulated API call, with human approval interrupts also part of the workflow. This is a useful architectural boundary: graph code establishes relationships, the model explains evidence, and explicit policy code decides whether an action is permitted.
Those safeguards are described by the project author; they have not been independently audited in the article. A design that includes policy checks and approval gates should not be mistaken for proof that an implementation is production-safe, immune to hallucinations, or appropriate for a bank’s regulatory obligations. Real deployment would require validation against the institution’s policies, data controls, operational procedures, and applicable legal requirements.
What results the author reports—and what they establish
Vyas reports that all 20 benchmark cases ran through one workflow, and that every model assertion linked to canonical evidence identifiers. The article also reports live dashboard updates and automated SAR report generation when the project’s stated thresholds and pattern criteria were met. These are self-reported outcomes; the article does not provide independent validation or enough methodology to establish performance beyond the project.
The author also reports an 85% reduction in prompt token costs after moving graph pathfinding and counting into GSQL. The visible article page says “Posted on Sep 24” without a year, so the publication year is not established there. No independent measurement method is presented for the cost reduction, and the figure should be read as this project’s reported result—not as a general expectation for TigerGraph, LangGraph, or fraud systems.
Practical lessons for designing a similar system
- Use graph queries for countable, testable relationship questions such as shared entities, path distance, and transaction flows.
- Make evidence provenance explicit so an analyst can inspect what supports each assertion.
- Represent missing evidence and confidence as workflow state, not just as caveats in generated prose.
- Keep recommendations distinct from authorization, and route consequential steps through deterministic rules and appropriate human approval.
- Record how recommendations change as evidence arrives, so the investigation remains reviewable.
- Publish benchmark methods alongside results; case counts, traceability claims, and token savings are difficult to assess without reproducible details.
Vyas’s project offers an example of how graph computation, agent workflow, language-model synthesis, and policy enforcement can be separated in a fraud investigation. Its architecture is informative; its reported benchmark and cost results remain claims from the project article rather than independently established performance.
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